Apparatus and methods for minting non-fungible tokens (NFTS) from user-specific products and data

ABSTRACT

An apparatus and method for generating NFTs from user-specific products and data, the apparatus including at least a processor, a memory communicatively connected to the at least processor, wherein the memory containing instructions configuring the at least processor to receive a data collection from a user, wherein the data collection comprising a plurality of user-specific data objects, assess a plurality of user categories as a function of the data collection, identify a value function as a function of the plurality of user-specific data objects and the plurality of user categories, optimize the value function to generate a ranked plurality of user-specific data objects, generate a recommendation for the NFT as a function of the ranked plurality of user-specific data objects, and generate the NFT as a function of the recommendation.

FIELD OF THE INVENTION

The present invention generally relates to the field of NFTs. Inparticular, the present invention is directed to apparatus and methodsfor minting NFTs from user-specific products and data.

BACKGROUND

The process of minting NFTs backed by physical and digital assetsbecomes practical to generate value. In addition, NFTs can conferdifferent types of rights to the assets for a specific purpose. However,identifying and maximizing the value of the NFTs using different typesof user data and user-specific products poses complex challenges.

SUMMARY OF THE DISCLOSURE

In one aspect an apparatus for generating a NFT from user-specificproducts and data is disclosed. The apparatus including at least aprocessor, a memory communicatively connected to the at least processor,wherein the memory containing instructions configuring the at leastprocessor to receive a data collection from a user, wherein the datacollection comprising a plurality of user-specific data objects, assessa plurality of user categories as a function of the data collection,identify a value function as a function of the plurality ofuser-specific data objects and the plurality of user categories,optimize the value function to generate a ranked plurality ofuser-specific data objects, generate a recommendation for the NFT as afunction of the ranked plurality of user-specific data objects, andgenerate the NFT as a function of the recommendation.

In another aspect a method for generating a NFT from user-specificproducts and data is disclosed. The method including receiving a datacollection from a user, wherein the data collection comprising aplurality of user-specific data objects, assessing a plurality of usercategories as a function of the data collection, identifying a valuefunction as a function of the plurality of user-specific data objectsand the plurality of user categories, optimizing the value function togenerate a ranked plurality of user-specific data objects, generating arecommendation for the NFT as a function of the ranked plurality ofuser-specific data objects, and generating the NFT as a function of therecommendation.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is block diagram of an apparatus for generating NFTs fromuser-specific products and data;

FIG. 2 is a block diagram of exemplary embodiment of an immutablesequential listing;

FIG. 3 is a block diagram of exemplary embodiment of a machine learningmodule;

FIG. 4 is a diagram of an exemplary nodal network;

FIG. 5 is a block diagram of an exemplary node;

FIG. 6 is a block diagram of a fuzzy set system;

FIG. 7 is a flow diagram illustrating a method of generating NFTs fromuser-specific products and data; and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed toapparatus and methods for generating NFTs from user-specific productsand data. In an embodiment, apparatus and methods may be used toidentify and maximize a value function for a user using user-specificproducts and/or data to generate NFTs.

Aspects of the present disclosure can be used to assess a plurality ofuser categories using assigned weights based on user profile and otheruser related data. Aspects of the present disclosure can also be used toidentify a value function configured for identifying value ofuser-specific product and/or data for a particular user. In addition,aspects of the present disclosure can be used to maximize the value ofuser-specific product and/or data for the particular user. Aspects ofthe present disclosure can also be used to convert user-specific productand/or data, including but not limited to, images, videos, audios,and/or digitization of physical products for the purposes of mintingNFTs. Further, aspects of the present disclosure can also be used tostore user-specific products and/or data in immutable sequentiallistings on decentralized platforms.

Exemplary embodiments illustrating aspects of the present disclosure aredescribed below in the context of several specific examples.

In an embodiment, methods and apparatuses described herein may performor implement one or more aspects of a cryptographic system. In oneembodiment, a cryptographic system is a system that converts data from afirst form, known as “plaintext,” which is intelligible when viewed inits intended format, into a second form, known as “ciphertext,” which isnot intelligible when viewed in the same way. Ciphertext may beunintelligible in any format unless first converted back to plaintext.In one embodiment, a process of converting plaintext into ciphertext isknown as “encryption.” Encryption process may involve the use of adatum, known as an “encryption key,” to alter plaintext. Cryptographicsystem may also convert ciphertext back into plaintext, which is aprocess known as “decryption.” Decryption process may involve the use ofa datum, known as a “decryption key,” to return the ciphertext to itsoriginal plaintext form. In embodiments of cryptographic systems thatare “symmetric,” decryption key is essentially the same as encryptionkey: possession of either key makes it possible to deduce the other keyquickly without further secret knowledge. Encryption and decryption keysin symmetric cryptographic systems may be kept secret and shared onlywith persons or entities that the user of the cryptographic systemwishes to be able to decrypt the ciphertext. One example of a symmetriccryptographic system is the Advanced Encryption Standard (“AES”), whicharranges plaintext into matrices and then modifies the matrices throughrepeated permutations and arithmetic operations with an encryption key.

In embodiments of cryptographic systems that are “asymmetric,” eitherencryption or decryption key cannot be readily deduced withoutadditional secret knowledge, even given the possession of acorresponding decryption or encryption key, respectively; a commonexample is a “public key cryptographic system,” in which possession ofthe encryption key does not make it practically feasible to deduce thedecryption key, so that the encryption key may safely be made availableto the public. An example of a public key cryptographic system is RSA,in which an encryption key involves the use of numbers that are productsof very large prime numbers, but a decryption key involves the use ofthose very large prime numbers, such that deducing the decryption keyfrom the encryption key requires the practically infeasible task ofcomputing the prime factors of a number which is the product of two verylarge prime numbers. Another example is elliptic curve cryptography,which relies on the fact that given two points P and Q on an ellipticcurve over a finite field, and a definition for addition where A+B=−R,the point where a line connecting point A and point B intersects theelliptic curve, where “0,” the identity, is a point at infinity in aprojective plane containing the elliptic curve, finding a number k suchthat adding P to itself k times results in Q is computationallyimpractical, given correctly selected elliptic curve, finite field, andP and Q. A further example of asymmetrical cryptography may includelattice-based cryptography, which relies on the fact that variousproperties of sets of integer combination of basis vectors are hard tocompute, such as finding the one combination of basis vectors thatresults in the smallest Euclidean distance. Embodiments of cryptography,whether symmetrical or asymmetrical, may include quantum-securecryptography, defined for the purposes of this disclosure ascryptography that remains secure against adversaries possessing quantumcomputers; some forms of lattice-based cryptography, for instance, maybe quantum-secure.

In some embodiments, apparatus and methods described herein producecryptographic hashes, also referred to by the equivalent shorthand term“hashes.” A cryptographic hash, as used herein, is a mathematicalrepresentation of a lot of data, such as files or blocks in a blockchain as described in further detail below; the mathematicalrepresentation is produced by a lossy “one-way” algorithm known as a“hashing algorithm.” Hashing algorithm may be a repeatable process; thatis, identical lots of data may produce identical hashes each time theyare subjected to a particular hashing algorithm. Because hashingalgorithm is a one-way function, it may be impossible to reconstruct alot of data from a hash produced from the lot of data using the hashingalgorithm. In the case of some hashing algorithms, reconstructing thefull lot of data from the corresponding hash using a partial set of datafrom the full lot of data may be possible only by repeatedly guessing atthe remaining data and repeating the hashing algorithm; it is thuscomputationally difficult if not infeasible for a single computer toproduce the lot of data, as the statistical likelihood of correctlyguessing the missing data may be extremely low. However, the statisticallikelihood of a computer of a set of computers simultaneously attemptingto guess the missing data within a useful timeframe may be higher,permitting mining protocols as described in further detail below.

In an embodiment, hashing algorithm may demonstrate an “avalancheeffect,” whereby even extremely small changes to lot of data producedrastically different hashes. This may thwart attempts to avoid thecomputational work necessary to recreate a hash by simply inserting afraudulent datum in data lot, enabling the use of hashing algorithms for“tamper-proofing” data such as data contained in an immutable ledger asdescribed in further detail below. This avalanche or “cascade” effectmay be evinced by various hashing processes; persons skilled in the art,upon reading the entirety of this disclosure, will be aware of varioussuitable hashing algorithms for purposes described herein. Verificationof a hash corresponding to a lot of data may be performed by running thelot of data through a hashing algorithm used to produce the hash. Suchverification may be computationally expensive, albeit feasible,potentially adding up to significant processing delays where repeatedhashing, or hashing of large quantities of data, is required, forinstance as described in further detail below. Examples of hashingprograms include, without limitation, SHA256, a NIST standard; furthercurrent and past hashing algorithms include Winternitz hashingalgorithms, various generations of Secure Hash Algorithm (including“SHA-1,” “SHA-2,” and “SHA-3”), “Message Digest” family hashes such as“MD4,” “MD5,” “MD6,” and “RIPEMD,” Keccak, “BLAKE” hashes and progeny(e.g., “BLAKE2,” “BLAKE-256,” “BLAKE-512,” and the like), MessageAuthentication Code (“MAC”)-family hash functions such as PMAC, OMAC,VMAC, HMAC, and UMAC, Poly1305-AES, Elliptic Curve Only Hash (“ECOH”)and similar hash functions, Fast-Syndrome-based (FSB) hash functions,GOST hash functions, the Grøstl hash function, the HAS-160 hashfunction, the JH hash function, the RadioGatún hash function, the Skeinhash function, the Streebog hash function, the SWIFFT hash function, theTiger hash function, the Whirlpool hash function, or any hash functionthat satisfies, at the time of implementation, the requirements that acryptographic hash be deterministic, infeasible to reverse-hash,infeasible to find collisions, and have the property that small changesto an original message to be hashed will change the resulting hash soextensively that the original hash and the new hash appear uncorrelatedto each other. A degree of security of a hash function in practice maydepend both on the hash function itself and on characteristics of themessage and/or digest used in the hash function. For example, where amessage is random, for a hash function that fulfillscollision-resistance requirements, a brute-force or “birthday attack”may to detect collision may be on the order of O(2^(n/2)) for n outputbits; thus, it may take on the order of 2256 operations to locate acollision in a 512 bit output “Dictionary” attacks on hashes likely tohave been generated from a non-random original text can have a lowercomputational complexity, because the space of entries they are guessingis far smaller than the space containing all random permutations ofbits. However, the space of possible messages may be augmented byincreasing the length or potential length of a possible message, or byimplementing a protocol whereby one or more randomly selected strings orsets of data are added to the message, rendering a dictionary attacksignificantly less effective.

Embodiments described in this disclosure may perform secure proofs. A“secure proof,” as used in this disclosure, is a protocol whereby anoutput is generated that demonstrates possession of a secret, such asdevice-specific secret, without demonstrating the entirety of thedevice-specific secret; in other words, a secure proof by itself, isinsufficient to reconstruct the entire device-specific secret, enablingthe production of at least another secure proof using at least adevice-specific secret. A secure proof may be referred to as a “proof ofpossession” or “proof of knowledge” of a secret. Where at least adevice-specific secret is a plurality of secrets, such as a plurality ofchallenge-response pairs, a secure proof may include an output thatreveals the entirety of one of the plurality of secrets, but not all ofthe plurality of secrets; for instance, secure proof may be a responsecontained in one challenge-response pair. In an embodiment, proof maynot be secure; in other words, proof may include a one-time revelationof at least a device-specific secret, for instance as used in a singlechallenge-response exchange.

Secure proof may include a zero-knowledge proof, which may provide anoutput demonstrating possession of a secret while revealing none of thesecret to a recipient of the output; zero-knowledge proof may beinformation-theoretically secure, meaning that an entity with infinitecomputing power would be unable to determine secret from output.Alternatively, zero-knowledge proof may be computationally secure,meaning that determination of secret from output is computationallyinfeasible, for instance to the same extent that determination of aprivate key from a public key in a public key cryptographic system iscomputationally infeasible. Zero-knowledge proof algorithms maygenerally include a set of two algorithms, a prover algorithm, or “P,”which is used to prove computational integrity and/or possession of asecret, and a verifier algorithm, or “V” whereby a party may check thevalidity of P. Zero-knowledge proof may include an interactivezero-knowledge proof, wherein a party verifying the proof must directlyinteract with the proving party; for instance, the verifying and provingparties may be required to be online, or connected to the same networkas each other, at the same time. Interactive zero-knowledge proof mayinclude a “proof of knowledge” proof, such as a Schnorr algorithm forproof on knowledge of a discrete logarithm. in a Schnorr algorithm, aprover commits to a randomness r, generates a message based on r, andgenerates a message adding r to a challenge c multiplied by a discretelogarithm that the prover is able to calculate; verification isperformed by the verifier who produced c by exponentiation, thuschecking the validity of the discrete logarithm. Interactivezero-knowledge proofs may alternatively or additionally include sigmaprotocols. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various alternative interactivezero-knowledge proofs that may be implemented consistently with thisdisclosure.

Alternatively, zero-knowledge proof may include a non-interactivezero-knowledge, proof, or a proof wherein neither party to the proofinteracts with the other party to the proof; for instance, each of aparty receiving the proof and a party providing the proof may receive areference datum which the party providing the proof may modify orotherwise use to perform the proof. As a non-limiting example,zero-knowledge proof may include a succinct non-interactive arguments ofknowledge (ZK-SNARKS) proof, wherein a “trusted setup” process createsproof and verification keys using secret (and subsequently discarded)information encoded using a public key cryptographic system, a proverruns a proving algorithm using the proving key and secret informationavailable to the prover, and a verifier checks the proof using theverification key; public key cryptographic system may include RSA,elliptic curve cryptography, ElGamal, or any other suitable public keycryptographic system. Generation of trusted setup may be performed usinga secure multiparty computation so that no one party has control of thetotality of the secret information used in the trusted setup; as aresult, if any one party generating the trusted setup is trustworthy,the secret information may be unrecoverable by malicious parties. Asanother non-limiting example, non-interactive zero-knowledge proof mayinclude a Succinct Transparent Arguments of Knowledge (ZK-STARKS)zero-knowledge proof. In an embodiment, a ZK-STARKS proof includes aMerkle root of a Merkle tree representing evaluation of a secretcomputation at some number of points, which may be 1 billion points,plus Merkle branches representing evaluations at a set of randomlyselected points of the number of points; verification may includedetermining that Merkle branches provided match the Merkle root, andthat point verifications at those branches represent valid values, wherevalidity is shown by demonstrating that all values belong to the samepolynomial created by transforming the secret computation. In anembodiment, ZK-STARKS does not require a trusted setup.

Zero-knowledge proof may include any other suitable zero-knowledgeproof. Zero-knowledge proof may include, without limitationbulletproofs. Zero-knowledge proof may include a homomorphic public-keycryptography (hPKC)-based proof. Zero-knowledge proof may include adiscrete logarithmic problem (DLP) proof. Zero-knowledge proof mayinclude a secure multi-party computation (MPC) proof. Zero-knowledgeproof may include, without limitation, an incrementally verifiablecomputation (IVC). Zero-knowledge proof may include an interactiveoracle proof (IOP). Zero-knowledge proof may include a proof based onthe probabilistically checkable proof (PCP) theorem, including a linearPCP (LPCP) proof. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms ofzero-knowledge proofs that may be used, singly or in combination,consistently with this disclosure.

In an embodiment, secure proof is implemented using a challenge-responseprotocol. In an embodiment, this may function as a one-time padimplementation; for instance, a manufacturer or other trusted party mayrecord a series of outputs (“responses”) produced by a device possessingsecret information, given a series of corresponding inputs(“challenges”), and store them securely. In an embodiment, achallenge-response protocol may be combined with key generation. Asingle key may be used in one or more digital signatures as described infurther detail below, such as signatures used to receive and/or transferpossession of crypto-currency assets; the key may be discarded forfuture use after a set period of time. In an embodiment, varied inputsinclude variations in local physical parameters, such as fluctuations inlocal electromagnetic fields, radiation, temperature, and the like, suchthat an almost limitless variety of private keys may be so generated.Secure proof may include encryption of a challenge to produce theresponse, indicating possession of a secret key. Encryption may beperformed using a private key of a public key cryptographic system, orusing a private key of a symmetric cryptographic system; for instance,trusted party may verify response by decrypting an encryption ofchallenge or of another datum using either a symmetric or public-keycryptographic system, verifying that a stored key matches the key usedfor encryption as a function of at least a device-specific secret. Keysmay be generated by random variation in selection of prime numbers, forinstance for the purposes of a cryptographic system such as RSA thatrelies prime factoring difficulty. Keys may be generated by randomizedselection of parameters for a seed in a cryptographic system, such aselliptic curve cryptography, which is generated from a seed. Keys may beused to generate exponents for a cryptographic system such asDiffie-Helman or ElGamal that are based on the discrete logarithmproblem.

Embodiments described in this disclosure may utilize, evaluate, and/orgenerate digital signatures. A “digital signature,” as used herein,includes a secure proof of possession of a secret by a signing device,as performed on provided element of data, known as a “message.” Amessage may include an encrypted mathematical representation of a fileor other set of data using the private key of a public key cryptographicsystem. Secure proof may include any form of secure proof as describedabove, including without limitation encryption using a private key of apublic key cryptographic system as described above. Signature may beverified using a verification datum suitable for verification of asecure proof; for instance, where secure proof is enacted by encryptingmessage using a private key of a public key cryptographic system,verification may include decrypting the encrypted message using thecorresponding public key and comparing the decrypted representation to apurported match that was not encrypted; if the signature protocol iswell-designed and implemented correctly, this means the ability tocreate the digital signature is equivalent to possession of the privatedecryption key and/or device-specific secret. Likewise, if a messagemaking up a mathematical representation of file is well-designed andimplemented correctly, any alteration of the file may result in amismatch with the digital signature; the mathematical representation maybe produced using an alteration-sensitive, reliably reproduciblealgorithm, such as a hashing algorithm as described above. Amathematical representation to which the signature may be compared maybe included with signature, for verification purposes; in otherembodiments, the algorithm used to produce the mathematicalrepresentation may be publicly available, permitting the easyreproduction of the mathematical representation corresponding to anyfile.

In some embodiments, digital signatures may be combined with orincorporated in digital certificates. In one embodiment, a digitalcertificate is a file that conveys information and links the conveyedinformation to a “certificate authority” that is the issuer of a publickey in a public key cryptographic system. Certificate authority in someembodiments contains data conveying the certificate authority'sauthorization for the recipient to perform a task. The authorization maybe the authorization to access a given datum. The authorization may bethe authorization to access a given process. In some embodiments, thecertificate may identify the certificate authority. The digitalcertificate may include a digital signature.

In some embodiments, a third party such as a certificate authority (CA)is available to verify that the possessor of the private key is aparticular entity; thus, if the certificate authority may be trusted,and the private key has not been stolen, the ability of an entity toproduce a digital signature confirms the identity of the entity andlinks the file to the entity in a verifiable way. Digital signature maybe incorporated in a digital certificate, which is a documentauthenticating the entity possessing the private key by authority of theissuing certificate authority and signed with a digital signaturecreated with that private key and a mathematical representation of theremainder of the certificate. In other embodiments, digital signature isverified by comparing the digital signature to one known to have beencreated by the entity that purportedly signed the digital signature; forinstance, if the public key that decrypts the known signature alsodecrypts the digital signature, the digital signature may be consideredverified. Digital signature may also be used to verify that the file hasnot been altered since the formation of the digital signature.

Referring now to FIG. 1 , an exemplary embodiment of apparatus 100 forgenerating NFTs from user-specific products and data is illustrated.Apparatus 100 includes at least a processor 104 and memory 108communicatively connected to processor 104. Processor 104 may includeany computing device as described in this disclosure, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described in this disclosure.Computing device may include, be included in, and/or communicate with amobile device such as a mobile telephone or smartphone. Processor 104may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. Processor 104 may interface or communicate with one or moreadditional devices as described below in further detail via a networkinterface device. Network interface device may be utilized forconnecting processor 104 to one or more of a variety of networks, andone or more devices. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. Processor 104 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Processor 104 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Processor 104 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Processor 104 may be implemented usinga “shared nothing” architecture in which data is cached at the worker,in an embodiment, this may enable scalability of apparatus 100 and/orcomputing device.

Still referring to FIG. 1 , processor 104 may be designed and/orconfigured to perform any method, method step, or sequence of methodsteps in any embodiment described in this disclosure, in any order andwith any degree of repetition. For instance, processor 104 may beconfigured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Processor 104 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1 , as used in this disclosure,“communicatively connected” means connected by way of a connection,attachment, or linkage between two or more relata which allows forreception and/or transmittance of information therebetween. For example,and without limitation, this connection may be wired or wireless,direct, or indirect, and between two or more components, circuits,devices, systems, apparatus and the like, which allows for receptionand/or transmittance of data and/or signal(s) therebetween. Data and/orsignals therebetween may include, without limitation, electrical,electromagnetic, magnetic, video, audio, radio and microwave data and/orsignals, combinations thereof, and the like, among others. Acommunicative connection may be achieved, for example and withoutlimitation, through wired or wireless electronic, digital or analog,communication, either directly or by way of one or more interveningdevices or components. Further, communicative connection may includeelectrically coupling or connecting at least an output of one device,component, or circuit to at least an input of another device, component,or circuit. For example, and without limitation, via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connecting may also include indirect connections via, forexample and without limitation, wireless connection, radiocommunication, low power wide area network, optical communication,magnetic, capacitive, or optical coupling, and the like. In someinstances, the terminology “communicatively coupled” may be used inplace of communicatively connected in this disclosure.

Still referring to FIG. 1 , processor 104 is configured to receive adata collection 112 from a user. As used in this disclosure, “receive”means to accept, collect, or otherwise gather input from a user and/or adevice. As used in this disclosure, a “data collection” is an elementcontaining a data object 116 related to the user. In addition, a “dataobject,” as used in this disclosure, describes a single piece of data.In some cases, data collection 112 may include a plurality of dataobjects. In a non-limiting example, a data collection may be a stringcontaining a plurality of words, wherein each word may be a data object.In some cases, data collection 112 may be in various format such as,without limitation, txt file, JSON file, word document, pdf file, excelsheet, image, video, audio, and the like thereof. In other cases, datacollection 112 may be present in any data structure described in thisdisclosure. In some embodiments, without limitation, data collection 112may include personal information related to the user. In some cases,personal information may include, without limitation, user's name, age,gender, identification, profession, experience, social media posts,geographical information, family information, employer, and the likethereof. In some embodiments, without limitation, data collection 112may also include any finance information related to the user. In somecases, finance information may include, without limitation, assets,income, expense, debts, and the like thereof. In other embodiments, datacollection 112 may further include any health information related to theuser. In some cases, health information may include, without limitation,wellness, insurance, medical records, disease records, lifestyle, andthe like thereof. In a non-limiting example, processor 104 may receive adata collection in a text file format, wherein the data collection mayinclude user's personal information such as, without limitation, user'sname, age, gender, home address, and the like thereof.

With continued reference to FIG. 1 , in some embodiments, datacollection 112 may be present as a vector. As used in this disclosure, a“vector” is a data structure that represents one or more quantitativevalues and/or measures of data collection 112. A vector may berepresented as an n-tuple of values, where n is one or more values, asdescribed in further detail below; a vector may alternatively oradditionally be represented as an element of a vector space, defined asa set of mathematical objects that can be added together under anoperation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each value of n-tuple of values may represent a measurement orother quantitative value associated with a given category of data, orattribute, examples of which are provided in further detail below; avector may be represented, without limitation, in n-dimensional spaceusing an axis per category of value represented in n-tuple of values,such that a vector has a geometric direction characterizing the relativequantities of attributes in the n-tuple as compared to each other. Twovectors may be considered equivalent where their directions, and/or therelative quantities of values within each vector as compared to eachother, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent, for instance as measuredusing cosine similarity as computed using a dot product of two vectors;however, vector similarity may alternatively or additionally bedetermined using averages of similarities between like attributes, orany other measure of similarity suitable for any n-tuple of values, oraggregation of numerical similarity measures for the purposes of lossfunctions as described in further detail below. Any vectors as describedherein may be scaled, such that each vector represents each attributealong an equivalent scale of values. Each vector may be “normalized,” ordivided by a “length” attribute, such as a length attribute/as derivedusing a Pythagorean norm:

${l = \sqrt{{\sum}_{i = 0}^{n}a_{i}^{2}}},$where a_(i) is attributed number i of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes.

With continued reference to FIG. 1 , in some embodiments, datacollection 112 may be present as a dictionary. As used in thisdisclosure, a “dictionary” is a data structure containing an unorderedset of key value pairs. In this disclosure, a “key value pair” is a datarepresentation of a data element such as, without limitation,user-specific data object 116. For instance, in a non-limiting exemplaryembodiment, user-specific data object may include user profile, userclassification, user category, physical and/or digital assets owned bythe user, and user's financial and personal information, and the like.In some cases, dictionary may be an associative memory, or associativearrays, or the like thereof. In a non-limiting example, dictionary maybe a hash table. In an embodiment, kay value pair may include a uniquekey, wherein the unique kay may associate with one or more values. Inanother embodiment, key value pair may include a value, wherein thevalue may associate with a single key. In some cases, each key valuepair of set of key value pairs in dictionary may be separated by aseparator, wherein the separator is an element for separating two keyvalue pairs. In a non-limiting example, separator may be a comma inbetween each key value pairs of plurality of key value pairs withindictionary. In another non-limiting example, a dictionary may beexpressed as “{first key value pair, second key value pair},” whereinthe first key value pair and the second key value pair may be separateby a comma separator, and wherein both first key value pair and secondkey value pair may be expressed as “first/second key: first/secondvalue.” In a further non-limiting example, data collection 112 may bepresent as a dictionary: “{1: A, 2: B, 3: C},” wherein A may be a firstuser related data correspond to a first data object, B may be a seconduser related data correspond to a second data object, and C may be athird user related data correspond to a third data object. User-specificdata object 116 may include any kind of information related to the usersuch as, without limitation, user's personal information, financialinformation, health information, and the like thereof. Additionally, oralternatively, dictionary may include a term index, wherein the termindex is a data structure to facilitate fast lookup of user-specificdata object 116 in data collection 112 (i.e., index). In some cases,without limitation, term index may use a zero-based indexing, whereinthe zero-based indexing may configure dictionary to start with index 0.In some cases, without limitation, term index may use a one-basedindexing, wherein the one-based indexing may configure dictionary tostart with index 1. In other cases, without limitation, term index mayuse a n-based indexing, wherein the n-based indexing may configuredictionary to start with any index from 0 to n. Further, term index maybe determined/calculated using one or more hash algorithms. Hashalgorithms may be any hash algorithm described above in this disclosure.In a non-limiting example, data collection may be present as adictionary containing a plurality of hashes, wherein each hash ofplurality of hashes represents a single data object. Hash may be anycryptographic hash as described above in this disclosure.

With continued reference to FIG. 1 , in other embodiments, datacollection 112 may be present as any other data structure such as,without limitation, tuple, single dimension array, multi-dimensionarray, list, linked list, queue, set, stack, dequeue, stream, map,graph, tree, and the like thereof. In some embodiments, data collection112 may be present as a combination of more than one above datastructures. In a non-limiting example, data collection 112 may be adictionary of lists. As will be appreciated by persons having ordinaryskill in the art, after having read the entirety of this disclosure, theforegoing list is provided by way of example and other data structurescan be added as an extension or improvements of apparatus 100 disclosedherein. In some embodiments, without limitation, data collection 112 maybe an immutable data collection, wherein the immutable data collectionis a data collection that cannot be changed, modified, and/or updatedonce the data collection is received. In other embodiments, withoutlimitation, data collection 112 may be a mutable data collection,wherein the mutable data collection is a data collection that can bechanged, modified, and/or updated once the data collection is received.

With continued reference to FIG. 1 , in some cases, data objects relatedto the user within data collection 112 may be sorted in a certain ordersuch as, without limitation, ascending order, descending order, and thelike thereof. In some embodiments, without limitation, sorting dataobjects related to the user within data collection 112 may include usinga sorting algorithm. In some cases, sorting algorithm may include, butis not limited to, selection sort, bubble sort, insertion sort, mergesort, quick sort, heap sort, radix sort, and the like thereof. As willbe appreciated by persons having ordinary skill in the art, after havingread the entirety of this disclosure, the foregoing list is provided byway of example and other sorting algorithm can be added as an extensionor improvements of apparatus 100 disclosed herein.

With continued reference to FIG. 1 , additionally, or alternatively,data collection 112 may include an implicit data collection. As used inthis disclosure, an “implicit data collection” is data collection thatreceived by processor 104 in an implicit or non-invasive manner, wheredata can be collected automatically and/or with minimal attentions drawnfrom the user. In some cases, implicit data collection may include,without limitation, information regarding a user profile, a usercategory, user's facial activity, posture activity, event activity,vocal expression, language and choice of words, electrodermal activity,any other information that implies user's reliability, and the likethereof. In a non-limiting example, implicit data collection may includea fingerprint received through a digital fingerprinting, wherein thefingerprint is a unique identifier of one or more data objects 116, andwherein the digital fingerprinting is a computational process used toidentify and track user, apparatus 100, processor 104, and any otherdevices described in this disclosure online through a fingerprintingalgorithm. In some cases, fingerprint may include, without limitation,acoustic fingerprint, digital video fingerprint, browser fingerprint,and any other digital fingerprint, and the like thereof. In some cases,fingerprinting algorithm may include, without limitation, Rabin'salgorithm, hash algorithm described above, and the like thereof. Inanother non-limiting example, implicit data collection may include oneor more data objects 116 received through a network latency analysis,wherein the network latency analysis may provide information regardingto user's current network such as, without limitation, internetprotocol, internet protocol address, current domain name system,download speed, upload speed, round trip time (RTT), time to first byte(TTFB), and the like thereof.

With continued reference to FIG. 1 , data collection 112 may include oneor more data objects 116 that describes one or more signals. as used inthis disclosure, a “signal” is any intelligible representation of data,for example from one device to another. A signal may include an opticalsignal, a hydraulic signal, a pneumatic signal, a mechanical, signal, anelectric signal, a digital signal, an analog signal and the like. Insome cases, a signal may be used to communicate with a computing device,for example by way of one or more ports. In some cases, a signal may betransmitted and/or received by a computing device for example by way ofan input/output port. An analog signal may be digitized, for example byway of an analog to digital converter. In some cases, an analog signalmay be processed, for example by way of any analog signal processingsteps described in this disclosure, prior to digitization. In somecases, a digital signal may be used to communicate between two or moredevices, including without limitation computing devices. In some cases,a digital signal may be communicated by way of one or more communicationprotocols, including without limitation internet protocol (IP),controller area network (CAN) protocols, serial communication protocols(e.g., universal asynchronous receiver-transmitter [UART]), parallelcommunication protocols (e.g., IEEE 128 [printer port]), and the like.

With continued reference to FIG. 1 , in some embodiments, apparatus 100may perform one or more signal processing steps on a signal. Forinstance, apparatus 100 may analyze, modify, and/or synthesize a signalrepresentative of data in order to improve the signal, for instance byimproving transmission, storage efficiency, or signal to noise ratio.Exemplary methods of signal processing may include analog, continuoustime, discrete, digital, nonlinear, and statistical. Analog signalprocessing may be performed on non-digitized or analog signals.Exemplary analog processes may include passive filters, active filters,additive mixers, integrators, delay lines, compandors, multipliers,voltage-controlled filters, voltage-controlled oscillators, andphase-locked loops. Continuous-time signal processing may be used, insome cases, to process signals which varying continuously within adomain, for instance time. Exemplary non-limiting continuous timeprocesses may include time domain processing, frequency domainprocessing (Fourier transform), and complex frequency domain processing.Discrete time signal processing may be used when a signal is samplednon-continuously or at discrete time intervals (i.e., quantized intime). Analog discrete-time signal processing may process a signal usingthe following exemplary circuits sample and hold circuits, analogtime-division multiplexers, analog delay lines and analog feedback shiftregisters. Digital signal processing may be used to process digitizeddiscrete-time sampled signals. Commonly, digital signal processing maybe performed by a computing device or other specialized digitalcircuits, such as without limitation an application specific integratedcircuit (ASIC), a field-programmable gate array (FPGA), or a specializeddigital signal processor (DSP). Digital signal processing may be used toperform any combination of typical arithmetical operations, includingfixed-point and floating-point, real-valued and complex-valued,multiplication and addition. Digital signal processing may additionallyoperate circular buffers and lookup tables.

Still referring to FIG. 1 , in one embodiment, data collection 112 maycomprise at least a user profile comprising a plurality of user-relateddata associated with the user. For instance, in a non-limiting exemplaryembodiment, the user related data in the user profile may includeinformation associated with the user such as user's name, age, gender,identification, profession, experience, geographical information, familyinformation, employment history, financial information including income,assets, expense, and debts, and health, wellness, medical records,insurance, lifestyle, and the like thereof. As used in this disclosure,a “user” is a person or individual. In an embodiment, user profileand/or user related data may be obtained using a user device associatedwith the user. A “user device,” for the purpose of this disclosure, isany additional computing device, such as a mobile device, laptop,desktop computer, a tablet, or the like. In one embodiment, and withoutlimitation, a user device may be a computer and/or smart phone operatedby a user in a remote location. User device may include, withoutlimitation, a display; the display may include any display as describedin the entirety of this disclosure such as a light emitting diode (LED)screen, liquid crystal display (LCD), organic LED, cathode ray tube(CRT), touch screen, or any combination thereof. In a non-limitingembodiment, user device may include a graphical user interface (GUI)configured to display any information from apparatus 100.

Still referring to FIG. 1 , in some embodiments, in order to evaluateuser-specific products and/or data, user category 136 may be assessed asa function of data collection 112. Assessing the user category 136 mayinclude using a smart assessment. As used in this disclosure, a “usercategory” defines the class of a user. For instance, in someembodiments, a user is defined as a creator, a collector, acollaborator, and/or a community member. As used in this disclosure, a“smart assessment” is a set of questions and/or prompts that inquiresfor information related to user profile 124 and user-specific productsassociated with user category 136, wherein each question and/or promptmay lead to answers that affect user authentication, designation,verification, and any processing step described in this disclosure. Asused in this disclosure, a “user-specific product” includes physicaland/or digital assets that can be used to back an NFT. For instance,physical assets may include real estate, precious metals, consumergoods, collectables, and other commodities. Digital assets may includethe digitization of photos, videos, drawings, audio, virtual real estatein a metaverse, and a specific highlight of a life event. In someembodiments, questions within smart assessment may include selecting asection from plurality of selections as answer to reduce bias. In othercases, questions within smart assessment may include a free user inputas answer. In a non-limiting example, smart assessment may include aquestion asking the user regarding Intellectual Property (IP) ownership.For instance, the question may be “Dose the user/entity have all rightsin its intellectual property?” In some embodiments, smart assessment mayinclude questions such as “Is the user/entity the creator, collector,collaborator, and/or community member?” In some other embodiments, smartassessment may include questions such as “Does the user/entity plan tosell, rent, or license its IP?” In some embodiments, smart assessmentmay also include questions to facilitate a reasonable determination ofthe nature of the IP value (e.g., monetary value, personal value, andemotional value). In some cases, smart assessment may be in a form suchas, without limitation, a survey, transactional tracking, interview,report, events monitoring, and the like thereof. In some embodiments,smart assessment may include a data submission of one or moredocumentations from the user. In some embodiments, smart assessment maybe consistent with smart assessment in U.S. patent application Ser. No.17/984,912, filed on Nov. 10, 2022, and entitled, “APPARATUS AND METHODFOR VETTING USER USING A COMPUTING DEVICE,” which is incorporated byreference herein in its entirety.

Continuing to refer to FIG. 1 , in one embodiment, each user category136 includes an assigned weight. As used in this disclosure, an“assigned weight” (w_(i)) is a statistical weight assigned to each usercategory 136 (UC_(i)) based on one or more factors. In some embodiments,the one or more factors may include user profile 124 and/or dataassociated with each user profile 124. In an exemplary embodiment,different user category with different user profile and/or user-specificdata object is assigned a different weight. For instance, user categorywith more attributes in user profile 124 and/or user-specific product140 obtains a higher weight, whereas user category with less attributesin user profile 124 and/or user-specific product 140 obtains a lowerweight. As used in this disclosure, an “attribute” is considered afactor that is positively associated with user category 136. In someembodiments, attributes may be determined manually by a user. In otherembodiments, attributes may be determined automatically by processor 104as a result of a comparison of a plurality of attributes (e.g., userprofile, user intent, user experience, and/or the like) initiated byprocessor 104. In an exemplary embodiment, a weighted user category(UC_(weighted))=w_(I)*UC_(i). In one embodiment, w_(i) comprisespositive and negative values. A positive w_(i) indicates a positiveeffect on UC_(i), whereas a negative w_(i) indicates a positive effecton UC_(i). In other embodiments, w_(i) comprises weights that aregreater than zero, wherein the greater the weight the greater effects ithas on the weighted user category (UC_(weighted)).

Still referring to FIG. 1 , in some embodiments, user categoryassessment may involve the utilization of a user category assessmentmachine-learning model 128 trained by using user category assessmenttraining data 132 in order to output an assessment of user category 136.A “machine-learning model,” as used in this disclosure, involves aprocess that automatedly uses training data to generate an algorithmand/or model performed by processor 104 to produce outputs given dataprovided as inputs, for instance and without limitation as described infurther detail below. This is in contrast to a non-machine learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language. Training data,which may include any training data as described in further detailbelow, is data including correlations and/or examples usable by amachine learning algorithm to generate machine-learning models and/or tobe operated on by a lazy learning algorithm as described below.

Still referring to FIG. 1 , training data may be obtained may beobtained by processor 104 in any manner and/or form as describedanywhere in this disclosure, including and without limitation retrievingfrom data store 120 such as, without limitation, a database. Databasemay be implemented, without limitation, as a relational database, akey-value retrieval database such as a NOSQL database, or any otherformat or structure for use as a database that a person skilled in theart would recognize as suitable upon review of the entirety of thisdisclosure. Database may alternatively or additionally be implementedusing a distributed data storage protocol and/or data structure, such asa distributed hash table or the like. Database may include a pluralityof data entries and/or records as described above. Data entries in adatabase may be flagged with or linked to one or more additionalelements of information, which may be reflected in data entry cellsand/or in linked tables such as tables related by one or more indices ina relational database. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in which dataentries in a database may store, retrieve, organize, and/or reflect dataand/or records as used herein, as well as categories and/or populationsof data consistently with this disclosure. In some embodiments, datastore 120 may be a blockchain storage. In some embodiments, blockchainstorage may be a decentralized data store. In a non-limiting example,blockchain storage may be configured to save data such as, withoutlimitation, data collection 112 in a decentralized network, wherein thedecentralized network may distribute data collection 112 and/or datacollection processing across multiple devices. Data collectionprocessing may include any processing step described in this disclosure.In some embodiments, blockchain storage may be configured to processdata collection 112 through a sharding process, wherein the shardingprocess is a data store partitioning that separates into a plurality ofsmaller data stores known as data store shards. As used in thisdisclosure, a “data store shard” is a horizontal/vertical partition ofdata such as, without limitation, data collection 112 in a data storethat hold on a separate instance of the data store. Each shard may becopied to prevent data loss. Additionally, or alternatively, datacollection 112 may be encrypted with private key. Private key may be anyencryption key described above in this disclosure. Encryption of datacollection 112 may include any processing steps described anywhere inthis disclosure.

In a non-limiting example, processor 104 may train user categoryassessment machine-learning model 128 using user category assessmenttraining data 132, wherein user category assessment training data 132may include training data correlating user category to user profile. Inone embodiment, training data 132 may be collected from data manuallylabeled by an expert based on prior knowledge. Processor 104 may thenassess a plurality of user category 136 as a function of the traineduser category assessment machine-learning model 128.

Continuing to refer to FIG. 1 , machine-learning process may include aclassifier, which may classify inputs such as data objects 116 of datacollection 112 into user-specific products 140 and/or other types ofuser-specific data, and the like thereof. A “classifier,” as used inthis disclosure is a machine-learning model, such as a mathematicalmodel, neural net, or program generated by a machine learning algorithmknown as “classification algorithm,” as described in further detailbelow, that parses inputs into categories or bins of data, outputtingthe categories or bins of data and/or labels associated therewith. Aclassifier may be configured to output at least a datum that labels orotherwise identifies a set of data that are clustered together, found tobe close under a distance metric as described below, or the like.Processor 104 and/or another device may generate a classifier using aclassification algorithm, defined as a process whereby a processor 104derives a classifier from training data. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers. As a further non-limiting example,classification may be performed using a neural network classifier suchas without limitation a convolutional neural network-based classifier. Aconvolutional neural network is a neural network in which at least onehidden layer is a convolutional layer that convolves inputs to thatlayer with a subset of inputs known as a “kernel,” along with one ormore additional layers such as pooling layers, fully connected layers,and the like.

Still referring to FIG. 1 , processor 104 may be configured to generatea classifier using a Naïve Bayes classification algorithm. Naïve Bayesclassification algorithm generates classifiers by assigning class labelsto problem instances, represented as vectors of element values. Classlabels are drawn from a finite set. Naïve Bayes classification algorithmmay include generating a family of algorithms that assume that the valueof a particular element is independent of the value of any otherelement, given a class variable. Naïve Bayes classification algorithmmay be based on Bayes Theorem expressed as P(A/B)=P(B/A) P(A)÷P(B),where P(AB) is the probability of hypothesis A given data B also knownas posterior probability; P(B/A) is the probability of data B given thatthe hypothesis A was true; P(A) is the probability of hypothesis A beingtrue regardless of data also known as prior probability of A; and P(B)is the probability of the data regardless of the hypothesis. A naïveBayes algorithm may be generated by first transforming training datainto a frequency table. Processor 104 may then calculate a likelihoodtable by calculating probabilities of different data entries andclassification labels. Processor 104 may utilize a naïve Bayes equationto calculate a posterior probability for each class. A class containingthe highest posterior probability is the outcome of prediction. NaïveBayes classification algorithm may include a gaussian model that followsa normal distribution. Naïve Bayes classification algorithm may includea multinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1 , Processor 104 may be configured togenerate a classifier using a K-nearest neighbors (KNN) algorithm. A“K-nearest neighbors algorithm” as used in this disclosure, includes aclassification method that utilizes feature similarity to analyze howclosely out-of-sample-features resemble training data to classify inputdata to one or more clusters and/or categories of features asrepresented in training data; this may be performed by representing bothtraining data and input data in vector forms, and using one or moremeasures of vector similarity to identify classifications withintraining data, and to determine a classification of input data.K-nearest neighbors algorithm may include specifying a K-value, or anumber directing the classifier to select the k most similar entriestraining data to a given sample, determining the most common classifierof the entries in the database, and classifying the known sample; thismay be performed recursively and/or iteratively to generate a classifierthat may be used to classify input data as further samples. Forinstance, an initial set of samples may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship, whichmay be seeded, without limitation, using expert input received accordingto any process as described herein. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data. Heuristic may include selecting somenumber of highest-ranking associations and/or training data elements.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm:

${l = \sqrt{{\sum}_{i = 0}^{n}a_{i}^{2}}},$where a_(i) is attribute number i of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes; this may, for instance, be advantageous wherecases represented in training data are represented by differentquantities of samples, which may result in proportionally equivalentvectors with divergent values.

Still referring to FIG. 1 , a decision tree may be incorporated with oneor more machine-learning process to generate new data structures foruser profile 124 and user-specific product 140 and other types of data,which may be fed to user category assessment machine-learning model 128and value identification machine-learning model 144. In anothernon-limiting example, user category assessment machine-learning model128 and value identification machine-learning model 144 may include alanguage processing module and/or an image processing module configuredto extract one or more data objects, textual information, imageinformation, and the like from data collection 112. Language processingmodules and image processing modules may include any hardware and/orsoftware module. Language processing module may be configured toextract, from the one or more documents, one or more words. One or morewords may include, without limitation, strings of one or morecharacters, including without limitation any sequence or sequences ofletters, numbers, punctuation, diacritic marks, engineering symbols,geometric dimensioning and tolerancing (GD&T) symbols, chemical symbolsand formulas, spaces, whitespace, and other symbols, including anysymbols usable as textual data as described above. Image processingmodule may be configured using fuzzy sets to extract, from the one ormore documents, a plurality of features and/or elements associated withdata objects 116. Textual data and image data may be parsed into tokens,which may include a simple word (sequence of letters separated bywhitespace), or more generally a sequence of characters as describedpreviously, or pixels, a plurality of pixels, a fraction of adigitization of a user-specific product. The term “token,” as usedherein, refers to any smaller, individual groupings of text from alarger source of text; tokens may be broken up by word, pair of words,sentence, or other delimitation. These tokens may in turn be parsed invarious ways. Textual data may be parsed into words or sequences ofwords, which may be considered words as well. Textual data may be parsedinto “n-grams”, where all sequences of n consecutive characters areconsidered. Any or all possible sequences of tokens or words may bestored as “chains”, for example for use as a Markov chain or HiddenMarkov Model.

Continuing to refer to FIG. 1 , language processing module may operateto produce a language processing model. Language processing model mayinclude a program automatically generated by computing device and/orlanguage processing module to produce associations between one or morewords extracted from at least a document and detect associations,including without limitation mathematical associations, between suchwords. Associations between language elements, where language elementsinclude for purposes herein extracted words, relationships of suchcategories to other such term may include, without limitation,mathematical associations, including without limitation statisticalcorrelations between any language element and any other language elementand/or language elements. Statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating, for instance, a likelihood that a given extracted wordindicates a given category of semantic meaning. As a further example,statistical correlations and/or mathematical associations may includeprobabilistic formulas or relationships indicating a positive and/ornegative association between at least an extracted word and/or a givensemantic meaning; positive or negative indication may include anindication that a given document is or is not indicating a categorysemantic meaning. Whether a phrase, sentence, word, or other textualelement in a document or corpus of documents constitutes a positive ornegative indicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryat computing device, or the like.

With continued reference to FIG. 1 , language processing module and/ordiagnostic engine may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input terms and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted words, phrases, and/orother semantic units. There may be a finite number of categories towhich an extracted word may pertain; an HIM inference algorithm, such asthe forward-backward algorithm or the Viterbi algorithm, may be used toestimate the most likely discrete state given a word or sequence ofwords. Language processing module may combine two or more approaches.For instance, and without limitation, machine-learning program may use acombination of Naive-Bayes (NB), Stochastic Gradient Descent (SGD), andparameter grid-searching classification techniques; the result mayinclude a classification algorithm that returns ranked associations.

With continued reference to FIG. 1 , generating language processingmodel may include generating a vector space, which may be a collectionof vectors, defined as a set of mathematical objects that can be addedtogether under an operation of addition following properties ofassociativity, commutativity, existence of an identity element, andexistence of an inverse element for each vector, and can be multipliedby scalar values under an operation of scalar multiplication compatiblewith field multiplication, and that has an identity element isdistributive with respect to vector addition, and is distributive withrespect to field addition. Each vector in an n-dimensional vector spacemay be represented by an n-tuple of numerical values. Each uniqueextracted word and/or language element as described above may berepresented by a vector of the vector space. In an embodiment, eachunique extracted and/or other language element may be represented by adimension of vector space; as a non-limiting example, each element of avector may include a number representing an enumeration ofco-occurrences of the word and/or language element represented by thevector with another word and/or language element. Vectors may benormalized, scaled according to relative frequencies of appearanceand/or file sizes. In an embodiment associating language elements to oneanother as described above may include computing a degree of vectorsimilarity between a vector representing each language element and avector representing another language element; vector similarity may bemeasured according to any norm for proximity and/or similarity of twovectors, including without limitation cosine similarity, which measuresthe similarity of two vectors by evaluating the cosine of the anglebetween the vectors, which can be computed using a dot product of thetwo vectors divided by the lengths of the two vectors. Degree ofsimilarity may include any other geometric measure of distance betweenvectors.

With continued reference to FIG. 1 , language processing module may usea corpus of documents to generate associations between language elementsin a language processing module, and diagnostic engine may then use suchassociations to analyze words extracted from one or more documents anddetermine that the one or more documents indicate significance of acategory. In an embodiment, language module and/or processor 104 mayperform this analysis using a selected set of documents concerning userprofile 124 and/or user-specific product 140 and/or user-specific data,including documents identified by one or more experts as representinggood information; experts may identify or enter such documents viagraphical user interface such as visual interface 172, or maycommunicate identities of significant documents according to any othersuitable method of electronic communication, or by providing suchidentity to other persons who may enter such identifications intoprocessor 104. As used in this disclosure, “user-specific products” arephysical and/or digital assets that can be used to back an NFT. Forinstance, physical assets may include real estate, precious metals,consumer goods, collectables, and other commodities. Digital assets mayinclude the digitization of photos, videos, drawings, audio, virtualreal estate in a metaverse, and a specific highlight of a life event.Documents may be entered into a computing device by being uploaded by anexpert or other persons using, without limitation, file transferprotocol (FTP) or other suitable methods for transmission and/or uploadof documents; alternatively or additionally, where a document isidentified by a citation, a uniform resource identifier (URI), uniformresource locator (URL) or other datum permitting unambiguousidentification of the document, diagnostic engine may automaticallyobtain the document using such an identifier, for instance by submittinga request to a database or compendium of documents such as JSTOR asprovided by Ithaka Harbors, Inc. of New York.

With continued reference to FIG. 1 , processor 104 is further configuredto identify a value function 152 as a function of the plurality of aplurality of user category 136 and user-specific product 140 using avalue identification machine-learning model 144. As used in thisdisclosure, a “value function” is an algorithm and/or process used toidentify and evaluate user-specific product, including physical anddigital assets. As used in this disclosure, value identificationmachine-learning model 144 may use value identification training data148, one or more elements of smart assessment, and/or user category 136with assigned weights as inputs to output identifiable values for aparticular user, wherein value may include monetary value, and/or one ormore categories of personal and/or emotional value. For instance,monetary value may be defined by the dollar amount that would be paidfor an NFT backed by user-specific product 140. In some embodiments,personal and/or emotional value may be defined by a relationship betweenuser-specific product 140 and an owner of user-specific product 140. Inone embodiment, value identification training data 148 is collectedbased on historical transactions executed in similar user categories. Insome embodiments, value identification training data 148 may includeinputs including user-specific products 140 to correlated outputincluding values for user specific products 1340. In some embodiments,value identification training data 148 may correlate transactions ofvalue for user-specific products with known user categories to outputidentification of similar user-specific products that possess value. Inone embodiment, value identification training data 148 is manuallylabeled by experts. In some embodiments, value function 152 ismodifiable as a function of the one or more elements of smartassessment, which may include replacing, creating, or otherwise removingone or more questions and/or any other interaction components withinsmart assessment. In some embodiments, smart assessment may substitute anew set of questions in response to an assessed user category.

With continued reference to FIG. 1 , processor 104 is configured tooptimize value function 152 using a value maximization machine-learningmodel 156 to generate ranked user-specific products 160 and/or dataassociated with the user. In one embodiment, and without limitation,processor 104 may generate an optimization algorithm to compute a scoreassociated with each user-specific product 140 and select user-specificproduct 140 to maximize the score, depending on whether an optimalresult is represented by a maximal score. Value function 152, describedherein as an objective function, may be used by processor 104 to scoreeach possible user-specific product based on one or more objectives asdescribed below. In some embodiments, a score of a particularuser-specific product may be based on a combination of one or morefactors, including user profile, user intent, user experience,historical transactions executed by users classified in each usercategory (weighted or unweighted), percentage of ownership, and types ofvalue. Each factor may be assigned a score based on predeterminedvariables. In some embodiments, predetermined variables may be assignedmanually or automatically. Additionally, in some embodiments, theassigned scores may be weighted or unweighted.

Still referring to FIG. 1 , maximization of value function 152 mayinclude performing a greedy algorithm process. A “greedy algorithm,” asdisclosed herein, is defined as an algorithm that selects locallyoptimal choices, which may or may not generate a globally optimalsolution. For instance, processor 104 may select weighted user categoryso that scores associated therewith are the best score for eachuser-specific product. In other cases, processor 104 may select the typeof user-specific product and historical transactions executed on thesame type of user-specific product so that scores associated with thesetwo factors obtain the vest values for each user-specific product.

Continuing to refer to FIG. 1 , value function 152 may be formulated asa linear objective function, which processor 104 may solve using alinear program such as without limitation a mixed-integer program. A“linear program,” as used in this disclosure, is a program thatoptimizes a linear objective function, given at least a constraint. Forinstance, processor 104 may determine a weighted user category maximizesa total score subject to a percentage of user-specific productownership. A mathematical solver may be implemented to solve for theweighted user category associated with the type of value that concernseach user-specific product that maximizes the scores. Mathematicalsolver may be implemented on processor 104 and/or an external device,and/or may be implemented on a third-party solver.

With continued reference to FIG. 1 , optimizing value function 152 mayinclude minimizing a loss function, where a “loss function” is afunction that evaluates values and/or events of one or more variableswhich represent some “cost” associated with the events. In anon-limiting example, processor 104 may assign variables relating to anyof the above-described factors, calculate an output of mathematicalexpression using the variables, and select factors that produce anoutput having the lowest size, according to a given definition of“size,” of the set of outputs representing each of plurality of variouscombinations of the factors. Size may, for instance, includes absolutevalue, numerical size, or the like.

Still referring to FIG. 1 , to minimize the loss function, in oneembodiment, gradient descent may be incorporated with value maximizationmachine-learning model 156 for the maximization of the value ofuser-specific product 140. The gradient of a continuous function ƒ isdefined as a vector that contains a number of partial derivativesdƒ/dx_(i)(p) computed at a point p which represents a predeterminedproportional step size that dictates the frequency of thedifferentiation. The gradient is finite and defined if and only if allpartial derivatives are also defined and finite. “Gradient descent,” asdisclosed herein, is a first-order iterative process through which theparameters of a machine-learning model are optimized for finding a localminimum along a negative gradient. The gradient is calculated withrespect to the vector that contains the partial derivatives. Conversely,still referring to FIG. 1 , in one embodiment, gradient ascent isincorporated with value maximization machine-learning model 156 for thedetermination of a local maximum of value function 152 when valuefunction 152 is a concave function based on the predetermined magnitudeand/or step size of the factors associated with value function 152 asdescribed above.

Still referring to FIG. 1 , processor 104 is configured to generate arecommendation for NFT 164 as a function of ranked user-specificproducts 160, which is the output of value maximization machine-learningmodel 156. In one embodiment, the recommendation is generated based on ahighest value of NFT 164. For instance, processor 104 may be configuredto rank user-specific products 160 from highest to lowest based on theirpredicted value. In one embodiment, generating the ranked user-specificproducts 160 may include utilizing a fuzzy set system. In someembodiments, the user-specific products 160 may be ranked based on amarket model using fuzzy sets as described further below. A “marketmodel,” as used herein, is a model element of user data. For example,the market model may be training data models as described above.Processor 104 may generate and transmit a recommendation of the rankeduser-specific products 160 to interface 172, display, or a computingdevice separated by the user for user selection of user-specificproducts 160 to be minted into an NFT 164.

Still referring to FIG. 1 , processor 104 is configured to store rankeduser specific-products 160 and/or NFT 164 to an immutable sequentiallisting 168. Generated NFT 164 may be added to the immutable sequentiallisting 168 using a hash function, smarts contract, and/or any otherprocess as described above. In some embodiments, storing user-specificproducts 160 and/or NFT 164 to the immutable sequential listing 168 maybe deployed using a smart contract. A “smart contract,” as used in thisdisclosure, is an algorithm, data structure, and/or a transactionprotocol which automatically executes, controls, documents, and/orrecords legally relevant events and actions according to the terms of acontract or an agreement and assign ownership and manage thetransferability of the NFT 164 and/cryptocurrency. Objectives of smartcontracts may include reduction of need in trusted intermediators,arbitrations and enforcement costs, fraud losses, as well as thereduction of malicious and accidental exceptions. For example andwithout limitation, processor 104 may receive a user object 116 and/oruser-specific product 140 and broadcast it to and/or post it on ablockchain and/or immutable sequential listing 168 to trigger a smartcontract function; smart contract function in turn may create a tokenand assign it to its owner and/or creator, which may include an ownerand/or creator of creative work or an assignee and/or transfereethereof. Smart contracts may permit trusted transactions and agreementsto be carried out among disparate, anonymous parties without the needfor a central authority, legal system, or external enforcementmechanism. In a non-limiting embodiment, processor 104 may execute asmart contract to deploy NFT 164 from a user into immutable sequentiallisting 168. A smart contract may be configured to conform to variousstandards, such as ERC-721. A smart contract standard may providefunctionalities for smart contracts. As a further non-limiting example,a smart contract can contain and/or include in postings representationsof one or more agreed upon actions and/or transactions to be performed.A smart contract may contain and/or include payments to be performed,including “locked” payments that are automatically released to anaddress of a party upon performance of terms of contract. A smartcontract may contain and/or include in postings representations of itemsto be transferred, including without limitation, NFT 164 or cryptocurrencies. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of the various embodiments andimplementation of a smart contract for purposes as described herein.

Still referring to FIG. 1 , processor 104 may be configured to generatea NFT 164 as a function of a recommendation for the NFT 164. In oneembodiment, the recommendation for the NFT 164 is generated as afunction of the ranked plurality of user-specific products 160. A “NFT(non-fungible token),” as used in this disclosure, is a unique andnon-interchangeable unit of data stored on a digital ledger and/orimmutable sequential listing 168. NFT 164 may be associated withuser-specific products which may include physical goods, user-specificdata, and digitization of photos, videos, drawings, and audio. NFT 164may also be associated with physical assets such as real estate,collectables, and other commodities. An NFT 164 may represent all or aportion of data collection 112 as described further below. In someembodiments, the type and amount of data collection 112 that isrepresented in the NFT 164 may be determined by user profile 124 in usercategory 136. For instance, a creator or user may “tokenize” such assetsto be stored on a digital ledger and/or immutable sequential listing168, which may ensure non-duplicability and ownership, generate incomeby transferring ownership, rent, or license, and/or enable accessibilityof the assets. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of the various embodiments andpurposes of tokenizing an asset.

With continued reference to FIG. 1 , apparatus 100 may include adecentralized platform 176 for which the processor 104 and/or apparatus100 may operate on. A “decentralized platform,” as used in thisdisclosure, is a platform or server that enables secure data exchangebetween anonymous parties. Decentralized platforms may be supported byany blockchain technologies. For example, and without limitation,blockchain-supported technologies can potentially facilitatedecentralized coordination and alignment of human incentives on a scalethat only top-down, command-and-control structures previously could.“Decentralization,” as used in this disclosure, is the process ofdispersing functions and power away from a central location orauthority. In a non-limiting embodiment, decentralized platform can makeit difficult if not impossible to discern a particular center. In someembodiments, decentralized platform can include a decentralizedecosystem. Decentralized platform may serve as an ecosystem fordecentralized architectures such as an immutable sequential listing 168and/or blockchain.

In a non-limiting embodiment, and still referring to FIG. 1 ,decentralized platform may implement decentralized finance (DeFi).“Decentralized finance,” as used in this disclosure, as financialtechnology based on secure distributed ledgers similar. A decentralizedfinance architecture may include cryptocurrencies, software, andhardware that enables the development of applications. Defi offersfinancial instruments without relying on intermediaries such asbrokerages, exchanges, or banks. Instead, it uses smart contracts on ablockchain. DeFi platforms allow people to lend or borrow funds fromothers, speculate on price movements on assets using derivatives, tradecryptocurrencies, insure against risks, and earn interest insavings-like accounts. In some embodiments, DeFi uses a layeredarchitecture and highly composable building blocks. In some embodimentsDeFi platforms may allow creators and/or owners to lend or borrow fundsfrom others, trade cryptocurrencies and/or NFTs 164, insure againstrisks, and receive payments. In a non-limiting embodiment, Defi mayeliminate intermediaries by allowing creators, owners, collectors,and/or brokers to conduct financial transactions through peer-to-peerfinancial networks that use security protocols, connectivity, software,and hardware advancements. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of the variousembodiments of implementing decentralized finance for purposes asdescribed herein.

In a non-limiting embodiment, and still referring to FIG. 1 ,decentralized platform may implement Web 3.0. Whereas Web 2.0 is atwo-sided client-server architecture, with a business hosting anapplication and users (customers and advertisers), “Web 3.0,” as used inthis disclosure, is an idea or concept that decentralizes thearchitecture on open platforms. In some embodiments, decentralizedplatform may enable communication between a plurality of computingdevices, wherein it is built on a back-end of peer-to-peer,decentralized network of nodes, the applications run on decentralizedstorage systems rather than centralized servers. In some embodiments,these nodes may be comprised together to form a World Computer. A “WorldComputer,” as used in this disclosure, is a group of computing devicesthat are capable of automatically executing smart contract programs on adecentralized network. A “decentralized network,” as used in thisdisclosure, is a set of computing device sharing resources in which thearchitecture of the decentralized network distributes workloads amongthe computing devices instead of relying on a single central server. Ina non-limiting embodiment, a decentralized network may include an open,peer-to-peer, Turing-complete, and/or global system. A World Computerand/or apparatus 100 may be communicatively connected to immutablesequential listing 168. Any digitally signed assertions onto immutablesequential listing 168 may be configured to be confirmed by the WorldComputer. Alternatively or additionally, apparatus 100 may be configuredto store a copy of immutable sequential listing 168 into memory 108.This is so, at least in part, to process a digitally signed assertionthat has a better chance of being confirmed by the World Computer priorto actual confirmation. In a non-limiting embodiment, decentralizedplatform may be configured to tolerate localized shutdowns or attacks;it is censorship-resistant. In another non-limiting embodimentdecentralized platform and/or apparatus 100 may incorporate trustedcomputing. In a non-limiting example, because there is no one from whompermission is required to join the peer-to-peer network, as long as oneoperates according to the protocol; it is open-source, so itsmaintenance and integrity are shared across a network of engineers; andit is distributed, so there is no central server nor administrator fromwhom a large amount of value or information might be stolen. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of the various embodiments and functions of decentralizedplatform 176 for purposes as described herein.

With continued reference to FIG. 1 , decentralized platform 176 mayinclude a decentralized exchange platform. A “decentralized exchangeplatform,” as is used in this disclosure, contains digital technology,which allows buyers and sellers of securities such as NFTs 164 to dealdirectly with each other instead of meeting in a traditional exchange.In some embodiments, decentralized platform may include an NFTmarketplace. An “NFT marketplace” is a marketplace allowing uses totrade NFTs 164 and upload them to an address. Decentralized platform 176may act as any NFT marketplace such as, but not limited to, OpenSea,Polygon, FCTONE, The Sandbox, CryptoKitties, Dentraland, Nifty Gateway,VEEFreinds, ROCKI, SuperRare, Enjin Marketplace, Rarible, WazirX,Portion, Zora, Mintable, PlayDapp, Aavegotchi, and the like thereof.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of the various embodiments of a marketplace inthe context of NFTs 164.

Still referring to FIG. 1 , in some embodiments, apparatus 100 may useoptical character recognition to parse text, symbols, and the like fromdata collection 112. For example, optical character recognition may beused to recognize the names and numbers on a plurality of images.Optical character recognition may also be used to distinguish text andsymbols in data collection 116. Additionally, in some embodimentsapparatus 100 may also use an automatic speech recognition,machine-learning model to query and/or extract audio from datacollection 116. For example, processor 104 may receive a voice commandthrough interface 172 instructing the classification of audio data thatmatches the sound spoken by the user and/or a phrase spoken by the user.In one non-limiting embodiment, the utilization of optical characterrecognition and automatic speech recognition may be consistent with theutilization of optical character recognition and automatic speechrecognition in U.S. patent application Ser. No. 17/984,571, filed onNov. 10, 2022, and entitled, “AN APPARATUS AND FOR METHOD FOR MINTINGNFTS FROM USER SPECIFIC MOMENTS,” which is incorporated by referenceherein in its entirety.

Referring now to FIG. 2 , an exemplary embodiment of an immutablesequential listing 200 is illustrated. An “immutable sequentiallisting,” as used in this disclosure, is a data structure that placesdata entries in a fixed sequential arrangement, such as a temporalsequence of entries and/or blocks thereof, where the sequentialarrangement, once established, cannot be altered or reordered. Animmutable sequential listing 200 may be, include and/or implement animmutable ledger, where data entries that have been posted to immutablesequential listing 200 cannot be altered. Data elements are listing inimmutable sequential listing 200; data elements may include any form ofdata, including textual data, image data, encrypted data,cryptographically hashed data, and the like. Data elements may include,without limitation, one or more at least a digitally signed assertions.In one embodiment, a digitally signed assertion 204 is a collection oftextual data signed using a secure proof as described in further detailbelow; secure proof may include, without limitation, a digital signatureas described above. Collection of textual data may contain any textualdata, including without limitation American Standard Code forInformation Interchange (ASCII), Unicode, or similar computer-encodedtextual data, any alphanumeric data, punctuation, diacritical mark, orany character or other marking used in any writing system to conveyinformation, in any form, including any plaintext or cyphertext data; inan embodiment, collection of textual data may be encrypted, or may be ahash of other data, such as a root or node of a Merkle tree or hashtree, or a hash of any other information desired to be recorded in somefashion using a digitally signed assertion 204. In an embodiment,collection of textual data states that the owner of a certaintransferable item represented in a digitally signed assertion 204register is transferring that item to the owner of an address. Adigitally signed assertion 204 may be signed by a digital signaturecreated using the private key associated with the owner's public key, asdescribed above.

Still referring to FIG. 2 , a digitally signed assertion 204 maydescribe a transfer of a NFT and/or virtual currency, such ascrypto-currency as described below. The virtual currency may be adigital currency. Item of value may be a transfer of trust, for instancerepresented by a statement vouching for the identity or trustworthinessof the first entity. Item of value may be an interest in a fungiblenegotiable financial instrument representing ownership in a public orprivate corporation, a creditor relationship with a governmental body ora corporation, rights to ownership represented by an option, derivativefinancial instrument, commodity, debt-backed security such as a bond ordebenture or other security as described in further detail below. Aresource may be a physical machine e.g., a ride share vehicle or anyother asset. A digitally signed assertion 204 may describe the transferof a physical good; for instance, a digitally signed assertion 204 maydescribe the sale of a product. In some embodiments, a transfernominally of one item may be used to represent a transfer of anotheritem; for instance, a transfer of virtual currency may be interpreted asrepresenting a transfer of an access right; conversely, where the itemnominally transferred is something other than virtual currency, thetransfer itself may still be treated as a transfer of virtual currency,having value that depends on many potential factors including the valueof the item nominally transferred and the monetary value attendant tohaving the output of the transfer moved into a particular user'scontrol. The item of value may be associated with a digitally signedassertion 204 by means of an exterior protocol, such as the COLOREDCOINS created according to protocols developed by The Colored CoinsFoundation, the MASTERCOIN protocol developed by the MastercoinFoundation, or the ETHEREUM platform offered by the Stiftung EthereumFoundation of Baar, Switzerland, the Thunder protocol developed byThunder Consensus, or any other protocol.

Still referring to FIG. 2 , in one embodiment, an address is a textualdatum identifying the recipient of virtual currency or another item ofvalue in a digitally signed assertion 204. In some embodiments, addressis linked to a public key, the corresponding private key of which isowned by the recipient of a digitally signed assertion 204. Forinstance, address may be the public key. Address may be arepresentation, such as a hash, of the public key. Address may be linkedto the public key in memory of a computing device, for instance via a“wallet shortener” protocol. Where address is linked to a public key, atransferee in a digitally signed assertion 204 may record a subsequent adigitally signed assertion 204 transferring some or all of the valuetransferred in the first a digitally signed assertion 204 to a newaddress in the same manner. A digitally signed assertion 204 may containtextual information that is not a transfer of some item of value inaddition to, or as an alternative to, such a transfer. For instance, asdescribed in further detail below, a digitally signed assertion 204 mayindicate a confidence level associated with a distributed storage nodeas described in further detail below.

In an embodiment, and still referring to FIG. 2 immutable sequentiallisting 200 records a series of at least a posted content in a way thatpreserves the order in which the at least a posted content took place.Temporally sequential listing may be accessible at any of varioussecurity settings; for instance, and without limitation, temporallysequential listing may be readable and modifiable publicly, may bepublicly readable but writable only by entities and/or devices havingaccess privileges established by password protection, confidence level,or any device authentication procedure or facilities described herein,or may be readable and/or writable only by entities and/or deviceshaving such access privileges. Access privileges may exist in more thanone level, including, without limitation, a first access level orcommunity of permitted entities and/or devices having ability to read,and a second access level or community of permitted entities and/ordevices having ability to write; first and second community may beoverlapping or non-overlapping. In an embodiment, posted content and/orimmutable sequential listing 200 may be stored as one or more zeroknowledge sets (ZKS), Private Information Retrieval (PIR) structure, orany other structure that allows checking of membership in a set byquerying with specific properties. Such database may incorporateprotective measures to ensure that malicious actors may not query thedatabase repeatedly in an effort to narrow the members of a set toreveal uniquely identifying information of a given posted content.

Still referring to FIG. 2 , immutable sequential listing 200 maypreserve the order in which the at least a posted content took place bylisting them in chronological order; alternatively or additionally,immutable sequential listing 200 may organize digitally signedassertions 204 into sub-listings 208 such as “blocks” in a blockchain,which may be themselves collected in a temporally sequential order;digitally signed assertions 204 within a sub-listing 208 may or may notbe temporally sequential. The ledger may preserve the order in which atleast a posted content took place by listing them in sub-listings 208and placing the sub-listings 208 in chronological order. Immutablesequential listing 200 may be a distributed, consensus-based ledger,such as those operated according to the protocols promulgated by RippleLabs, Inc., of San Francisco, Calif., or the Stellar DevelopmentFoundation, of San Francisco, Calif, or of Thunder Consensus. In someembodiments, the ledger is a secured ledger; in one embodiment, asecured ledger is a ledger having safeguards against alteration byunauthorized parties. The ledger may be maintained by a proprietor, suchas a system administrator on a server, that controls access to theledger; for instance, the user account controls may allow contributorsto the ledger to add at least a posted content to the ledger, but maynot allow any users to alter at least a posted content that have beenadded to the ledger. In some embodiments, ledger is cryptographicallysecured; in one embodiment, a ledger is cryptographically secured whereeach link in the chain contains encrypted or hashed information thatmakes it practically infeasible to alter the ledger without betrayingthat alteration has taken place, for instance by requiring that anadministrator or other party sign new additions to the chain with adigital signature. Immutable sequential listing 200 may be incorporatedin, stored in, or incorporate, any suitable data structure, includingwithout limitation any database, datastore, file structure, distributedhash table, directed acyclic graph or the like. In some embodiments, thetimestamp of an entry is cryptographically secured and validated viatrusted time, either directly on the chain or indirectly by utilizing aseparate chain. In one embodiment the validity of timestamp is providedusing a time stamping authority as described in the RFC 3161 standardfor trusted timestamps, or in the ANSI ASC x9.95 standard. In anotherembodiment, the trusted time ordering is provided by a group of entitiescollectively acting as the time stamping authority with a requirementthat a threshold number of the group of authorities sign the timestamp.

In some embodiments, and with continued reference to FIG. 2 , immutablesequential listing 200, once formed, may be inalterable by any party, nomatter what access rights that party possesses. For instance, immutablesequential listing 200 may include a hash chain, in which data is addedduring a successive hashing process to ensure non-repudiation. Immutablesequential listing 200 may include a block chain. In one embodiment, ablock chain is immutable sequential listing 200 that records one or morenew at least a posted content in a data item known as a sub-listing 208or “block.” An example of a block chain is the BITCOIN block chain usedto record BITCOIN transactions and values. Sub-listings 208 may becreated in a way that places the sub-listings 208 in chronological orderand link each sub-listing 208 to a previous sub-listing 208 in thechronological order so that any computing device may traverse thesub-listings 208 in reverse chronological order to verify any at least aposted content listed in the block chain. Each new sub-listing 208 maybe required to contain a cryptographic hash describing the previoussub-listing 208. In some embodiments, the block chain contains a singlefirst sub-listing 208 sometimes known as a “genesis block.”

Still referring to FIG. 2 , the creation of a new sub-listing 208 may becomputationally expensive; for instance, the creation of a newsub-listing 208 may be designed by a “proof of work” protocol acceptedby all participants in forming immutable sequential listing 200 to takea powerful set of computing devices a certain period of time to produce.Where one sub-listing 208 takes less time for a given set of computingdevices to produce the sub-listing 208 protocol may adjust the algorithmto produce the next sub-listing 208 so that it will require more steps;where one sub-listing 208 takes more time for a given set of computingdevices to produce the sub-listing 208 protocol may adjust the algorithmto produce the next sub-listing 208 so that it will require fewer steps.As an example, protocol may require a new sub-listing 208 to contain acryptographic hash describing its contents; the cryptographic hash maybe required to satisfy a mathematical condition, achieved by having thesub-listing 208 contain a number, called a nonce, whose value isdetermined after the fact by the discovery of the hash that satisfiesthe mathematical condition. Continuing the example, the protocol may beable to adjust the mathematical condition so that the discovery of thehash describing a sub-listing 208 and satisfying the mathematicalcondition requires more or less steps, depending on the outcome of theprevious hashing attempt. Mathematical condition, as an example, mightbe that the hash contains a certain number of leading zeros and ahashing algorithm that requires more steps to find a hash containing agreater number of leading zeros, and fewer steps to find a hashcontaining a lesser number of leading zeros. In some embodiments,production of a new sub-listing 208 according to the protocol is knownas “mining.” The creation of a new sub-listing 208 may be designed by a“proof of stake” protocol as will be apparent to those skilled in theart upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 2 , in some embodiments, protocol alsocreates an incentive to mine new sub-listings 208. The incentive may befinancial; for instance, successfully mining a new sub-listing 208 mayresult in the person or entity that mines the sub-listing 208 receivinga predetermined amount of currency. The currency may be fiat currency.Currency may be cryptocurrency as defined below. In other embodiments,incentive may be redeemed for particular products or services; theincentive may be a gift certificate with a particular business, forinstance. In some embodiments, incentive is sufficiently attractive tocause participants to compete for the incentive by trying to race eachother to the creation of sub-listings 208. Each sub-listing 208 createdin immutable sequential listing 200 may contain a record or at least aposted content describing one or more addresses that receive anincentive, such as virtual currency, as the result of successfullymining the sub-listing 208.

With continued reference to FIG. 2 , where two entities simultaneouslycreate new sub-listings 208, immutable sequential listing 200 maydevelop a fork; protocol may determine which of the two alternatebranches in the fork is the valid new portion of the immutablesequential listing 200 by evaluating, after a certain amount of time haspassed, which branch is longer. “Length” may be measured according tothe number of sub-listings 208 in the branch. Length may be measuredaccording to the total computational cost of producing the branch.Protocol may treat only at least a posted content contained the validbranch as valid at least a posted content. When a branch is foundinvalid according to this protocol, at least a posted content registeredin that branch may be recreated in a new sub-listing 208 in the validbranch; the protocol may reject “double spending” at least a postedcontent that transfer the same virtual currency that another at least aposted content in the valid branch has already transferred. As a result,in some embodiments the creation of fraudulent at least a posted contentrequires the creation of a longer immutable sequential listing 200branch by the entity attempting the fraudulent at least a posted contentthan the branch being produced by the rest of the participants; as longas the entity creating the fraudulent at least a posted content islikely the only one with the incentive to create the branch containingthe fraudulent at least a posted content, the computational cost of thecreation of that branch may be practically infeasible, guaranteeing thevalidity of all at least a posted content in immutable sequentiallisting 200.

Still referring to FIG. 2 , additional data linked to at least a postedcontent may be incorporated in sub-listings 208 in immutable sequentiallisting 200; for instance, data may be incorporated in one or morefields recognized by block chain protocols that permit a person orcomputer forming a at least a posted content to insert additional datain immutable sequential listing 200. In some embodiments, additionaldata is incorporated in an unspendable at least a posted content field.For instance, the data may be incorporated in an OP RETURN within theBITCOIN block chain. In other embodiments, additional data isincorporated in one signature of a multi-signature at least a postedcontent. In an embodiment, a multi-signature at least a posted contentis at least a posted content to two or more addresses. In someembodiments, the two or more addresses are hashed together to form asingle address, which is signed in the digital signature of the at leasta posted content. In other embodiments, the two or more addresses areconcatenated. In some embodiments, two or more addresses may be combinedby a more complicated process, such as the creation of a Merkle tree orthe like. In some embodiments, one or more addresses incorporated in themulti-signature at least a posted content are typical crypto-currencyaddresses, such as addresses linked to public keys as described above,while one or more additional addresses in the multi-signature at least aposted content contain additional data related to the at least a postedcontent; for instance, the additional data may indicate the purpose ofthe at least a posted content, aside from an exchange of virtualcurrency, such as the item for which the virtual currency was exchanged.In some embodiments, additional information may include networkstatistics for a given node of network, such as a distributed storagenode, e.g. the latencies to nearest neighbors in a network graph, theidentities or identifying information of neighboring nodes in thenetwork graph, the trust level and/or mechanisms of trust (e.g.certificates of physical encryption keys, certificates of softwareencryption keys, (in non-limiting example certificates of softwareencryption may indicate the firmware version, manufacturer, hardwareversion and the like), certificates from a trusted third party,certificates from a decentralized anonymous authentication procedure,and other information quantifying the trusted status of the distributedstorage node) of neighboring nodes in the network graph, IP addresses,GPS coordinates, and other information informing location of the nodeand/or neighboring nodes, geographically and/or within the networkgraph. In some embodiments, additional information may include historyand/or statistics of neighboring nodes with which the node hasinteracted. In some embodiments, this additional information may beencoded directly, via a hash, hash tree or other encoding.

With continued reference to FIG. 2 , in some embodiments, virtualcurrency is traded as a crypto-currency. In one embodiment, acrypto-currency is a digital, currency such as Bitcoins, Peercoins,Namecoins, and Litecoins. Crypto-currency may be a clone of anothercrypto-currency. The crypto-currency may be an “alt-coin.”Crypto-currency may be decentralized, with no particular entitycontrolling it; the integrity of the crypto-currency may be maintainedby adherence by its participants to established protocols for exchangeand for production of new currency, which may be enforced by softwareimplementing the crypto-currency. Crypto-currency may be centralized,with its protocols enforced or hosted by a particular entity. Forinstance, crypto-currency may be maintained in a centralized ledger, asin the case of the XRP currency of Ripple Labs, Inc., of San Francisco,Calif. In lieu of a centrally controlling authority, such as a nationalbank, to manage currency values, the number of units of a particularcrypto-currency may be limited; the rate at which units ofcrypto-currency enter the market may be managed by a mutuallyagreed-upon process, such as creating new units of currency whenmathematical puzzles are solved, the degree of difficulty of the puzzlesbeing adjustable to control the rate at which new units enter themarket. Mathematical puzzles may be the same as the algorithms used tomake productions of sub-listings 208 in a block chain computationallychallenging; the incentive for producing sub-listings 208 may includethe grant of new crypto-currency to the miners. Quantities ofcrypto-currency may be exchanged using at least a posted content asdescribed above.

Referring now to FIG. 3 , an exemplary embodiment of a machine-learningmodule 300 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 304 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 308 given data provided as inputs 312;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 3 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 304 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 304 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 304 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 304 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 304 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 304 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data304 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 3 ,training data 304 may include one or more elements that are notcategorized; that is, training data 304 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 304 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 304 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 304 used by machine-learning module 300 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 3 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 316. Training data classifier 316 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 300 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 304. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers.

Still referring to FIG. 3 , machine-learning module 300 may beconfigured to perform a lazy-learning process 320 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 304. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 304 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 3 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 324. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 324 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 324 may be generated by creating an artificialneural network, such as a convolutional neural network including aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 304set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 3 , machine-learning algorithms may include atleast a supervised machine-learning process 328. At least a supervisedmachine-learning process 328, as defined herein, include algorithms thatreceive a training set relating several inputs to outputs, and seek tofind one or more mathematical relations relating inputs to outputs,where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm mayinclude inputs and outputs described through this disclosure, and ascoring function representing a desired form of relationship to bedetected between inputs and outputs; scoring function may, for instance,seek to maximize the probability that a given input and/or combinationof elements inputs is associated with a given output to minimize theprobability that a given input is not associated with a given output.Scoring function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 304. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 328 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 3 , machine learning processes may include atleast an unsupervised machine-learning processes 332. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 3 , machine-learning module 300 may be designedand configured to create a machine-learning model 324 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g., a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of one divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g., a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 3 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 4 , an exemplary embodiment of neural network 400is illustrated. A neural network 400 also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes 404, one or more intermediate layers 408, and an output layer ofnodes 412. Connections between nodes may be created via the process of“training” the network, in which elements from a training dataset areapplied to the input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning. Connections may run solely from input nodes toward outputnodes in a “feed-forward” network or may feed outputs of one layer backto inputs of the same or a different layer in a “recurrent network.”

Referring now to FIG. 5 , an exemplary embodiment of a node of a neuralnetwork is illustrated. A node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function co, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above.

Referring now to FIG. 6 , an exemplary embodiment of fuzzy setcomparison 600 is illustrated. A first fuzzy set 604 may be represented,without limitation, according to a first membership function 608representing a probability that an input falling on a first range ofvalues 612 is a member of the first fuzzy set 604, where the firstmembership function 608 has values on a range of probabilities such aswithout limitation the interval [0,1], and an area beneath the firstmembership function 608 may represent a set of values within first fuzzyset 604. Although first range of values 612 is illustrated for clarityin this exemplary depiction as a range on a single number line or axis,first range of values 612 may be defined on two or more dimensions,representing, for instance, a Cartesian product between a plurality ofranges, curves, axes, spaces, dimensions, or the like. First membershipfunction 608 may include any suitable function mapping first range 612to a probability interval, including without limitation a triangularfunction defined by two linear elements such as line segments or planesthat intersect at or below the top of the probability interval. As anon-limiting example, triangular membership function may be defined as:

${y\left( {x,a,b,c} \right)} = \left\{ \begin{matrix}{0,{{{for}\ x} > {c\ {and}\ x} < a}} \\{\frac{x - a}{b - a},\ {{{for}\ a} \leq x < b}} \\{\frac{c - x}{c - b},\ {{{if}\ b} < x \leq c}}\end{matrix} \right.$a trapezoidal membership function may be defined as:

${y\left( {x,a,b,c,d} \right)} = {\max\left( {{\min\ \left( {\frac{x - a}{b - a},\ 1,\frac{d - x}{d - c}} \right)},\ 0} \right)}$a sigmoidal function may be defined as:

${y\left( {x,a,c} \right)} = \frac{1}{1 - e^{- {a({x - c})}}}$a Gaussian membership function may be defined as:

${y\left( {x,c,\sigma} \right)} = e^{{- \frac{1}{2}}{(\frac{x - c}{\sigma})}^{2}}$and a bell membership function may be defined as:

${y\left( {x,a,b,c,} \right)} = \left\lbrack {1 + {❘\frac{x - c}{a}❘}^{2b}} \right\rbrack^{- 1}$Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various alternative or additionalmembership functions that may be used consistently with this disclosure.

Still referring to FIG. 6 , first fuzzy set 604 may represent any valueor combination of values as described above, including output from oneor more machine-learning models. A second fuzzy set 616, which mayrepresent any value which may be represented by first fuzzy set 604, maybe defined by a second membership function 620 on a second range 624;second range 624 may be identical and/or overlap with first range 612and/or may be combined with first range via Cartesian product or thelike to generate a mapping permitting evaluation overlap of first fuzzyset 604 and second fuzzy set 616. Where first fuzzy set 604 and secondfuzzy set 616 have a region 628 that overlaps, first membership function608 and second membership function 620 may intersect at a point 632representing a probability, as defined on probability interval, of amatch between first fuzzy set 604 and second fuzzy set 616.Alternatively or additionally, a single value of first and/or secondfuzzy set may be located at a locus 636 on first range 612 and/or secondrange 624, where a probability of membership may be taken by evaluationof first membership function 608 and/or second membership function 620at that range point. A probability at 628 and/or 632 may be compared toa threshold 640 to determine whether a positive match is indicated.Threshold 640 may, in a non-limiting example, represent a degree ofmatch between first fuzzy set 604 and second fuzzy set 616, and/orsingle values therein with each other or with either set, which issufficient for purposes of the matching process; for instance, thresholdmay indicate a sufficient degree of overlap between an output from oneor more machine-learning models and/or user-specific products and/or anassessed user category, alone or in combination. Alternatively oradditionally, each threshold may be tuned by a machine-learning and/orstatistical process, for instance and without limitation as described infurther detail below.

Further referring to FIG. 6 , in an embodiment, a degree of matchbetween fuzzy sets may be used to classify user-specific data objects,such as user profile and user-specific products. For instance, if a userprofile has a fuzzy set that matches a user category fuzzy set by havinga degree of overlap exceeding a threshold, processor 104 may classifythe user profile as one of the user categories. Where multiple fuzzymatches are performed, degrees of match for each respective fuzzy setmay be computed and aggregated through, for instance, addition,averaging, or the like, to determine an overall degree of match.

Still referring to FIG. 6 , in an embodiment, a user profile and/oruser-specific product may be compared to multiple user category fuzzysets. For instance, user profile may be represented by a fuzzy set thatis compared to each of the multiple user category fuzzy sets; and adegree of overlap exceeding a threshold between the user profile fuzzyset and any of the multiple user category fuzzy sets may cause processor104 to classify the user profile as belonging to user category. Forinstance, in one embodiment there may be two user category fuzzy sets,representing respectively a first user category and a second usercategory. First user category may have a first fuzzy set; and Seconduser category may have a second fuzzy set. Processor 104, for example,may compare a user profile fuzzy set with each of the first and seconduser category fuzzy sets, as described above, and classify a userprofile to either, both, or neither of the first and second usercategories. Machine-learning methods as described throughout may, in anon-limiting example, generate coefficients used in fuzzy set equationsas described above, such as without limitation x, c, and a of a Gaussianset as described above, as outputs of machine-learning methods.Likewise, user profile and/or user-specific products may be usedindirectly to determine a fuzzy set, as user-specific data objects fuzzyset may be derived from outputs of one or more machine-learning modelsthat take the user-specific data objects such as products and/or datadirectly or indirectly as inputs.

Still referring to FIG. 6 , a computing device may use a logiccomparison program, such as, but not limited to, a fuzzy logic model todetermine a correlation between a plurality of user-specific dataobjects and a plurality of user categories. A correlation betweenuser-specific data objects and user categories may include, but is notlimited to, irrelevant, poor, average, high, and the like; each suchdesignation may be represented as a value for a linguistic variablerepresenting correlation, or in other words, a fuzzy set as describedabove that corresponds to a degree of positive correlations ascalculated using any statistical, machine-learning, or other method thatmay occur to a person skilled in the art upon reviewing the entirety ofthis disclosure. In other words, a given element of user-specific dataobject may have a first non-zero value for membership in a firstlinguistic variable value such as a high correlation and a secondnon-zero value for membership in a second linguistic variable value suchas average correlation. In some embodiments, determining a correlationmay include using a linear regression model. A linear regression modelmay include a machine learning model. A linear regression model may betrained using a machine learning process. A linear regression model maymap statistics such as, but not limited to, degree of similarity withrespect to the type of user-specific data objects and user categories.In some embodiments, determining a correlation between user-specificdata objects and user categories may include using a classificationmodel. The classification model may be configured to input collecteddata and cluster data to a centroid based on, but not limited to,frequency of appearance, linguistic indicators of correlation, and thelike.

Centroids may include scores assigned to them such that level ofcorrelation of user-specific data objects and user categories may eachbe assigned a score. In some embodiments, the classification model mayinclude a K-means clustering model. In some embodiments, theclassification model may include a particle swarm optimization model. Insome embodiments, determining the classification model may include usinga fuzzy inference engine. A fuzzy inference engine may be configured tomap one or more elements of user-specific object data and user categorydata using fuzzy logic. In some embodiments, user-specific data objectsand user categories may be arranged by a logic comparison program intovarious level of correlation arrangements. A “correlation arrangement”as used in this disclosure is any grouping of objects and/or data basedon degree of match based on user category assessment. This step may beimplemented as described above in FIGS. 1-5 . Membership functioncoefficients and/or constants as described above may be tuned accordingto classification and/or clustering algorithms. For instance, andwithout limitation, a clustering algorithm may determine a Gaussian orother distribution of questions about a centroid corresponding to agiven level, and an iterative or other method may be used to find amembership function, for any membership function type as describedabove, that minimizes an average error from the statistically determineddistribution, such that, for instance, a triangular or Gaussianmembership function about a centroid representing a center of thedistribution that most closely matches the distribution. Error functionsto be minimized, and/or methods of minimization, may be performedwithout limitation according to any error function and/or error functionminimization process and/or method as described in this disclosure.

Referring now to FIG. 7 , a flow diagram of an exemplary method 700 forgenerating a NFT from user-specific product and data is illustrated. Atstep 705, method 700 includes receiving a data collection from a user,wherein the data collection comprising a plurality of user-specific dataobjects. In some embodiments, the user-specific data objects may includeuser-specific products and/or data, including but not limited to,images, videos, audios, and/or digitization of physical products for thepurpose of minting NFTs. In some embodiments, the data collection alsoincludes user profile and information associated with the user. This maybe implemented as described and with reference to FIGS. 1-6 . In someembodiments, data collection may be transmitted from a data store. Atstep 710, method 700 includes assessing a plurality of user categoriesas a function of the data. In one embodiment, assessing the plurality ofuser categories includes using a smart assessment. In some embodiment,each of the plurality of user categories carries an assigned weightconfigured for identifying and optimizing a value function. In anothernon-limiting embodiment, each of the plurality of user categoriesincludes a user designation based on the user profile. For instance, andwithout limitation, user designation and user profile may be consistentwith user designation and user profile in U.S. patent application Ser.No. 17/984,678, filed on Nov. 10, 2022, and entitled, “APPARATUS ANDMETHOD FOR GENERATING USER-SPECIFIC SELF-EXECUTING RECORDS,” which isincorporated by reference herein in its entirety.

Still referring to FIG. 7 , at step 715, method 700 includes identifyinga value function as a function of the plurality of user-specific dataobjects and the plurality of user categories. In one embodiment, andwithout limitation, identifying the value function as a function of theplurality of user-specific data objects and the plurality of usercategories comprises utilizing a value identification machine-learningmodel. This may be implemented as described and with reference to FIGS.1-6 .

Continuing to refer to FIG. 7 , at step 720, method 700 includesoptimizing the value function to generate a ranked plurality ofuser-specific data objects. In one embodiment, and without limitation,optimizing the value function to generate a ranked plurality ofuser-specific data objects includes utilizing a value maximizationmachine-learning model. In one embodiment, generating a ranked pluralityof user-specific data objects may include utilizing fuzzy sets and theranked plurality of user-specific data objects are configured to bestored in an immutable sequential listing on a decentralized platform.In some embodiments, the plurality of user-specific data objects areranked based on the optimized value function. This may be implemented asdescribed and with reference to FIGS. 1-6 .

With continued reference to FIG. 7 , at step 725, method 700 includesgenerating a recommendation for the NFT as a function of the rankedplurality of user-specific data objects. This may be implemented asdescribed and with reference to FIGS. 1-6 . At step 730, method 700includes generating the NFT as a function of the recommendation. Thismay be implemented as described and with reference to FIGS. 1-6 .

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 852 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 852 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 856. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An apparatus for generating a non-fungible token(NFT) from user-specific products and data, the apparatus comprising: atleast a processor; and a memory communicatively connected to the atleast processor, wherein the memory containing instructions configuringthe at least processor to: receive a data collection from a user,wherein the data collection comprises a plurality of user-specific dataobjects; assess a plurality of user categories as a function of the datacollection; identify a value function as a function of the plurality ofuser-specific data objects and the plurality of user categories;optimize the value function to generate a ranked plurality ofuser-specific data objects; generate a recommendation for an NFT as afunction of the ranked plurality of user-specific data objects; andgenerate the NFT as a function of the recommendation.
 2. The apparatusof claim 1, wherein the data collection comprises at least a userprofile comprising a plurality of user-related data associated with theuser.
 3. The apparatus of claim 1, wherein assessing the plurality ofuser categories comprises using a smart assessment.
 4. The apparatus ofclaim 3, wherein each of the plurality of user categories comprises anassigned weight.
 5. The apparatus of claim 1, wherein the plurality ofuser-specific data objects comprises at least one user-specific productassociated with the user.
 6. The apparatus of claim 1, whereinidentifying the value function as a function of the plurality ofuser-specific data comprises determining a correlation between theplurality of user-specific data objects and the plurality of usercategories using fuzzy sets.
 7. The apparatus of claim 1, wherein theNFT is generated based on a maximized value of the ranked plurality ofuser-specific data objects.
 8. The apparatus of claim 1, wherein theranked plurality of user-specific data objects is configured to bestored in an immutable sequential listing on a decentralized platform.9. The apparatus of claim 1, wherein identifying the value functioncomprises utilizing a value identification machine-learning model tocorrelate the plurality of user-specific data objects with the pluralityof user categories to output an identification of value of theuser-specific data objects.
 10. The apparatus of claim 1, whereinoptimizing the value function comprises utilizing a value maximizationmachine-learning model to correlate the plurality of user-specific dataobjects with an optimized score to output a ranked plurality ofuser-specific data objects.
 11. A method for generating a non-fungibletoken (NFT) from user-specific product and data, the method comprising:receiving, by at least a processor, a data collection from a user,wherein the data collection comprising a plurality of user-specific dataobjects; assessing, by the at least a processor, a plurality of usercategories as a function of the data collection; identifying, by the atleast a processor, a value function as a function of the plurality ofuser-specific data objects and the plurality of user categories;optimizing, by the at least a processor, the value function to generatea ranked plurality of user-specific data objects; generating, by the atleast a processor, a recommendation for the NFT as a function of theranked plurality of user-specific data objects; and generating, by theat least a processor, the NFT as a function of the recommendation. 12.The method of claim 11, wherein the data collection comprises at least auser profile comprising a plurality of user related data associated withthe user.
 13. The method of claim 11, wherein assessing the plurality ofuser categories comprises using a smart assessment.
 14. The method ofclaim 13, wherein each of the plurality of user categories comprises anassigned weight.
 15. The method of claim 11, wherein the plurality ofuser-specific data objects comprises at least one user-specific productassociated with the user.
 16. The method of claim 11, whereinidentifying the value function as a function of the plurality ofuser-specific data comprises determining a correlation between pluralityof user-specific data objects and the plurality of user categories usingfuzzy sets.
 17. The method of claim 11, wherein the NFT is generatedbased on a maximized value of the ranked plurality of user-specific dataobjects.
 18. The method of claim 11, wherein the ranked plurality ofuser-specific data objects is configured to be stored in an immutablesequential listing on a decentralized platform.
 19. The method of claim11, wherein identifying the value function comprises utilizing a valueidentification machine-learning model to correlate the plurality ofuser-specific data objects with the plurality of user categories tooutput an identification of value of the user-specific data objects. 20.The method of claim 11, wherein optimizing the value function comprisesutilizing a value maximization machine-learning model to correlate theplurality of user-specific data objects with an optimized score tooutput a ranked plurality of user-specific data objects.